This article was updated 14th December 2021.

Observability isn't a new thing, but it's amazing. Maybe you've heard about it, but if you're new to the concept, don't worry, we've got your back.

In this article you learn what observability in IT is and how it can transform your operations. Investing in observability can create powerful insights on how you manage your systems. This is because it can help you understand what's happening inside of a system by observing the outside of the system - you don't have to ship new codes to answer new questions.

What is Observability?

Let's take a moment to think about the average company's data infrastructure. There are age-old legacy systems, modernised cloud platforms, loads of Kubernetes containers, and a variety of microservices and interconnected open-source components. In other words, things are really, really complicated. Now, let's add a dash of pressure to the pot. Since 2020, over 80 percent of customer interactions have become digitised. And virtually every business is pursuing more intense digital transformation initiatives. The complexity is spiraling.

This brings up a question. How do you monitor your architecture across all of these systems? Better yet, how do you bridge all of these divergent pieces of digital real estate together to create one holistic window into your IT operations? Observability refers to your IT team's ability to "observe" and monitor interactions across all of these different systems and cloud environments. Typically, this is done through one (or all) of the five core pillars of observability:

  1. Monitoring
  2. Logging
  3. Tracing
  4. Analytics
  5. Alerting


While observability exists when data becomes available from the systems that you want to monitor, monitoring is the part of the process responsible for collecting such data. To improve this stage of Observability, ensure you answer questions such as:

  • What tools can help simplify the process? Are you using platforms such as Splunk or Citrix?
  • Do you need Synthetic Monitoring or Real User Monitoring?
  • What you should watch?
  • How long should you keep data? You don't need to keep all data all the time. Time series data is helpful, but too much data can be wasteful and unnecessary.
  • How should you aggregate the data for simple processing?


Logging is simple to implement but difficult to get it right. It defines where your services and servers place their debugging and execution information and also what you want to log and how you want to log it. Logging may also define how these logs can get transformed during shipping to a searchable and/or aggregation system. The following need answers in connection to logging:

  • How much logging is appropriate for each service?
  • Where should all logs ship to?
  • What tools can simplify this process?
  • How long should you retain logs in your shipped location and locally?
  • Are you populating logs with the correct data?
  • Do you need to transform logs to make them more ingestible and useful?


This tenet defines what is happening or what happened. It is a very proactive tenet that's often overlooked. To ensure workable tracing, ask the following questions:

  • Is there visibility end to end for transactions?
  • What code can you add to a service to get a better insight into execution behavior (anomaly hunting and debugging )?
  • What failures are critical enough to alert immediately?


Analytics is the heart of observability. It captures almost all other tenets to create a better understanding of the system. The relevant questions to analytics are:

  • Are graphs highlighting what is abnormal? Are they showing trends?
  • What do you want to see from your data and what is it telling me?
  • Are applications experiencing snarl-up?
  • Based on the analytics, what needs adjustment in your environment?
  • Is data complete for decision-making?
  • Is scaling responsive enough?


Finally, alerting is about who should get notified in case of an actionable event, and how to carry out the notification. It is important to get it right. The following questions are pertinent:

  • What alerts require attention? Remove the ones that don't require attention.
  • Is the process of resolving alerts automated?
  • Are you tracking alerts for trends?

Implementing Proactive and Actionable Observability: what to avoid?

In the process of implementing observability, there are certain mistakes you need to avoid, these include:

  • Getting alerts on everything
  • Monitoring everything
  • Using default graphs
  • Storing all data and all logs

How to Avoid These Mistakes

All these mistakes will make your inboxes to fill with ignored alert notifications. Your logging system will get overloaded to an extent that it becomes difficult to sort and filter for useful information. This will cause zero observability and alert complacency. But worry not. Here are some tips to help you avoid these mistakes:

  1. Determine what kinds of performance, metrics, and information you want and then add codes to the application to eliminate what you don't need. 
  2. Condense nonsense alert - get rid of alerts that aren't actionable
  3. Ensure operations and development contribute to observability.

What's next?

Observability is a way to gain insight from your environment by getting the most out of the tenets discussed above. It involves building services and applications that help in shedding the noise and providing targeted visibility. Observability involves analyzing information and turning data into system improvements.

For full implementation of the observability tenets, you need the input of IT plataforms like Splunk and 2 Steps. Want to learn more about this topic? Click here to read our article on how 2 Steps together with Splunk can help your observability strategy.


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